Clawdemy Lessons

An orientation to support vector machines: what the maximum-margin idea and kernel trick teach, prerequisites, and the learning outcomes for this lesson.

Brief for the random forests lesson: what you will learn, how forests fit after decision trees, prerequisites, and read time before you start.

Overview of the logistic regression lesson: what you will learn, how it builds on linear regression and gradient descent, prerequisites, and time needed.

An orientation to k-means clustering: what you will learn, where unsupervised learning fits, prerequisites, and the time and difficulty to expect.

A study guide to gradient descent: what it is, where it fits after linear regression, and the skills you will have by the end.

Orientation for the hierarchical clustering lesson: what a dendrogram is, the prerequisite, learning outcomes, and read and practice time.

What the linear regression lesson covers, where it fits among classical machine learning methods, the prerequisites, and the time and difficulty.

Overview of the decision trees lesson: what you will learn, how it builds on logistic regression and sets up random forests, prerequisites, and time.

A quick orientation to boosting: what you'll learn, how it builds on random forests and gradient descent, prerequisites, and time and difficulty.

Overview of the architecture lesson: the decoder-only skeleton, converged design choices, sizing hyperparameters, prerequisites, math level, and time.

Orientation to the from-scratch LLM track: what you'll learn, prerequisites, the math involved, and time and difficulty for building the tokenizer.

What this lesson on model cost covers: FLOPs and the 6ND rule, the 16N memory estimate, arithmetic intensity, and einops, plus prerequisites, math, and timing.

Overview, prerequisites, and learning goals for the lesson on attention's cost problems, the MQA, GQA, and sliding-window alternatives, and mixture of experts.

A working picture of transformers before you run any: the tokens-in-tokens-out idea, why they replaced RNNs, the three shapes, and where Hugging Face fits.

Overview of the handwritten-digit lesson: what you'll learn, where it fits in the track, prerequisites (none), and the read and practice time.

Overview of the lesson on AI agents: the perceive-decide-act loop definition, the four parts that make a system agentic, prerequisites, and what comes next.

An overview of writing tool definitions an AI agent can use reliably: the four parts, when-to-use and negative guidance, and disambiguating overlapping tools.

An overview of agent planning: why reactive agents fail at scale, decomposition into ordered sub-tasks, plan-then-execute versus replanning, and grain size.

Overview of the multi-agent systems lesson: what it covers, prerequisites, learning outcomes, and how it fits after planning in the agent design track.

What the lesson on agent self-checks covers: why reflection raises reliability, the forms it takes, how it compares to adding an agent, and its honest limits.

Orientation for the tool-use lesson: what you will learn, prerequisites, the four-step tool-call exchange in brief, and how it fits the agents track.

Overview of how agents hold information: short-term context versus persistent memory, what is worth retaining, and the context, staleness, and privacy costs.

Overview of the hand-roll-versus-framework decision, the framework landscape by category, and choosing by fit so you stay free of any single library.

A guided overview of the six ways AI agents fail on their own, the guardrail that contains each, and when to require human-in-the-loop confirmation.

Overview of agentic RAG: how making retrieval a tool the agent decides to call turns the fixed retrieve-read-answer pipeline into a reasoning loop.